Succinct Homomorphic Secret Sharing

This work introduces homomorphic secret sharing (HSS) with succinct share size. In HSS, private inputs are shared between parties, who can then homomorphically evaluate a function on their shares, obtaining a share of the function output. In succinct HSS, a portion of the inputs can be distributed using shares whose size is sublinear in the number of such inputs. The parties can then locally evaluate a function $f$ on the shares, with the restriction that $f$ must be linear in the succinctly shared inputs.

We construct succinct, two-party HSS for branching programs, based on either the decisional composite residuosity assumption, a DDH-like assumption in class groups, or learning with errors with a superpolynomial modulus-to-noise ratio. We then give several applications of succinct HSS, which were only previously known using fully homomorphic encryption, or stronger tools:

• Succinct vector oblivious linear evaluation (VOLE): Two parties can obtain secret shares of a long, arbitrary vector $\vec x$, multiplied by a scalar $\Delta$, with communication sublinear in the size of the vector.
• Batch, multi-party distributed point functions: A protocol for distributing a batch of secret, random point functions among $N$ parties, for any polynomial $N$, with communication sublinear in the number of DPFs.
• Sublinear MPC for any number of parties: Two new constructions of MPC with sublinear communication complexity, with $N$ parties for any polynomial $N$: (1) For general layered Boolean circuits of size $s$, with communication $O(N s/\log\log s)$, and (2) For layered, sufficiently wide Boolean circuits, with communication $O(N s/\log s)$.